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• Statistical Inference 统计推断
• Statistical Computing 统计计算
• (Generalized) Linear Models 广义线性模型
• Statistical Machine Learning 统计机器学习
• Longitudinal Data Analysis 纵向数据分析
• Foundations of Data Science 数据科学基础

Probability adaption, also known as sequence learning, is another important application of BN. It is used for probability updating based on the new information accumulated over time where the new information could be the occurrences of certain BEs or the accident precursors. The critical events for failure of ED were identified by probability updating, and their occurrences were recorded over a period of time in this study. A hypothetical case, in which the occurrences of identified critical events in the South China Sea have been recorded for a time interval of 15 days over one year (Tables 6,7,8, and 9), is included as an example to illustrate how to realize probability adaption. Normally, the average drilling cycle for a well is about 3 months. For this reason, the occurrence record of critical events is incorporated with the seasonal weather considerations in Liuhua oil field in the South China Sea. It is worth noting that the occurrence record of critical BEs represents whether the events occur or not, and it is not the actual occurrence record of the events. This means that the critical events occur once the ED is initiated each time. For example, $\mathrm{X} 9$ refers to the critical event of midstroke of TJ being not configured. If this event occurs during the installation of drilling riser system, it will exist during the drilling cycle of the well all along. However, for the basic event of X20 (formation of natural gas hydrate around the LMRP connector), as the hydrate can be removed by ROV once it is observed, the occurrence record may change during the drilling cycle.
The prior probabilities can be adapted after occurrences of these critical events for each well, and the revised prior probabilities $P$ can be calculated as follows [5, $41,42]$ :
$$P=\frac{a+s}{n+s}$$
where $a$ and $n$ denote the occurrence records of ED failure and total records of ED operations, respectively, for the past wells, and $s$ represents the occurrence record of the critical events for the ongoing well.

## 统计代写|贝叶斯网络代写Bayesian network代考|Model Validation

When a new methodology is developed, it requires a careful validation to ensure its robustness. A sensitivity analysis was carried out in this study to test the proposed model. If the model is robust, the obtained result would be sensitive, but would not show abrupt variations to any minor change of the input parameters $[2,43]$. With the assumption of the prior probability of critical BE X15 being increased by $10 \%$, the probability of ED success decreased from $95.09$ to $94.97 \%$. When both of the prior probabilities of critical BEs X15 and X17 were increased by $10 \%$, the probability of ED success decreased from $94.97$ to $94.91 \%$. When the prior probabilities of critical BEs X15, X17, and X22 were increased by $10 \%$ simultaneously, the probability of ED success decreased from $94.91$ to $94.81 \%$. As expected, a slight increment of prior probabilities for critical BEs induced the decrement of probability of ED success in a reasonable way, thus giving a validation of the model.

In the present study, four reasons for ED of DP drilling vessel were analyzed which include drift-off, drive-off, storms, and internal solitary waves. Based on the analysis of reasons for ED, the hazards of ED were identified, which include human, design, operation, time, equipment, and control factors, and the influencing mechanism of the hazardous factors on ED was investigated.

Considering the specialty of ED operations and the simplicity of accident evolution process of ED failure, failure probability analysis of drilling riser ED operations was carried out by BN approach. Six categories of influencing factors were modeled and integrated into FT. Accident evolution process and three end states induced by ED failure were modeled by ESD, and then the integrated FT-ESD model was mapped into the $\mathrm{BN}$ which could consider polymorphism of BEs as well as conditional dependencies among the primary events of the ED operations. However, BN approach demands more expertise in terms of prior probability, conditional probability, and network construction based on causal relationships between components. A BN model also helps to identify the most probable path of events leading to an ED failure and the most probable paths of consequence caused by ED failure.

The present study indicates that the methodology proposed herein is an alternative approach in failure probability analysis of ED operations for deepwater drilling risers. The study showed that the X15 (inadequate overpull), X17 (large inclination angle of wellhead) and X22 (unreasonable disconnect moment) were the first three critical BEs for the failure of ED operations, and the probabilities of ED failure and potential consequences caused by ED failure varied with the states of critical BEs. Overall, the failure of ED operations is an event with low occurrence probability, and blowout is the consequence with the lowest occurrence probability caused by failure of ED operations. The analysis results obtained from this study could provide reference for risk decision making of $\mathrm{ED}$ operations and a better vision of ED safety issues.

# 贝叶斯网络代考

## 统计代写|贝叶斯网络代写Bayesian network代考|概率自适应

$$P=\frac{a+s}{n+s}$$

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## MATLAB代写

MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中，其中问题和解决方案以熟悉的数学符号表示。典型用途包括：数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发，包括图形用户界面构建MATLAB 是一个交互式系统，其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题，尤其是那些具有矩阵和向量公式的问题，而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问，这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展，得到了许多用户的投入。在大学环境中，它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域，MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要，工具箱允许您学习应用专业技术。工具箱是 MATLAB 函数（M 文件）的综合集合，可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。

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